NVIDIA NCP-ADS Actual Tests : NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are a data scientist working on a large-scale deep learning project. Your team needs to train a deep neural network (DNN) on terabytes of image data using NVIDIA GPUs in the cloud.
Which of the following approaches will maximize GPU utilization and optimize training time in an NVIDIA- accelerated cloud environment?

A. Use Jupyter Notebooks on a local machine with minimal GPU resources and transfer model checkpoints manually to the cloud for training.
B. Use NVIDIA NGC containers with TensorFlow or PyTorch and enable NCCL for multi-GPU communication.
C. Select low-cost, consumer-grade GPUs available in the cloud marketplace to reduce costs.
D. Avoid using mixed precision training and rely solely on full-precision (FP32) computations for numerical stability.

Question 2

You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A. Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.
B. Store all data as CSV files and perform ETL operations using traditional row-based processing.
C. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
D. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.

Question 3

You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A. NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
B. NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
C. NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
D. NVIDIA Nsight Systems for managing dataset storage and retrieval performance.

Question 4

Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Log Transformation
B. Min-Max Scaling
C. Z-score Standardization
D. Robust Scaling

Question 5

A data scientist is working with datasets ranging from hundreds of megabytes to several terabytes and needs to select the most efficient NVIDIA-accelerated data processing library for optimal memory management and performance.
Which approach is best for selecting the appropriate library for different dataset sizes?

A. Always use Dask regardless of dataset size since Dask automatically scales from small to large datasets.
B. Use RAPIDS cuML for handling all data processing tasks, regardless of dataset size.
C. Use Pandas for all dataset sizes since Pandas has built-in multi-threading optimizations.
D. Use cuDF for small datasets and Dask-cuDF for larger datasets that do not fit in a single GPU's memory.

Solutions: